Examining the performance of topic modeling techniques in Twitter trends extraction

Mutia N. Kurniati, Woo Jong Ryu, Md Hijbul Alam, Sang-Geun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

It is very important to extract the Twitter trends since it reflects the personal view over 645 million of its users. We examine the effectiveness of two topic modeling techniques i.e., standard Latent Dirichlet Allocation (LDA) and semantic-based Joint Multi-grain Topic-Sentiment (JMTS) in Twitter trends extraction. In addition, we also examine the frequent phrase method. Our finding reveals that JMTS significantly outperforms frequent phrase method and LDA by 54% and 24%, respectively.

Original languageEnglish
Title of host publicationInternational Conference on Information Networking 2014, ICOIN 2014
PublisherIEEE Computer Society
Pages364-369
Number of pages6
ISBN (Print)9781479936892
DOIs
Publication statusPublished - 2014
Event2014 28th International Conference on Information Networking, ICOIN 2014 - Phuket, Thailand
Duration: 2014 Feb 102014 Feb 12

Publication series

NameInternational Conference on Information Networking
ISSN (Print)1976-7684

Other

Other2014 28th International Conference on Information Networking, ICOIN 2014
CountryThailand
CityPhuket
Period14/2/1014/2/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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